Pyramid Consulting provides rewarding career opportunities for Consultants and enables our Clients to become more agile and competitive through the innovative use of technology, people and process.  Founded in 1996 and headquartered in Atlanta, GA, Pyr...

Pyramid Consulting, Inc

Job Profile: Data Science / Machine Learning

Job Type: Full-Time Nomina based job Opportunity

Location: 100% Remote in Mexico

 

Job Description:

 

 

"""We have an urgent requirement for a Data Scientist, preferably with Space Tech experience but not mandatory.# Data Scientist preferably with specialization in Geospatial / Remote Sensing

 

 

\## About the role We are building a satellite-based early-warning system that detects wind-driven sand encroachment and pipeline displacement across a desert pipeline network, using Sentinel-1 (SAR) and Sentinel-2 (optical) imagery, foundation models, and physical dune-migration modelling. You will own the detection and calibration science: turning raw satellite passes into calibrated, validated alerts against real field-logged events.

 

 

\## What you will do - Build and calibrate change-detection and anomaly models on multi-temporal Sentinel-1/2 imagery over pipeline corridors. - Learn per-site """"normal terrain"""" baselines and validate detections against a ground-truth event log (detection rate, lead time, false-positive rate, AUC). - Fine-tune geospatial foundation models (Prithvi-EO / similar) with LoRA/PEFT on limited labelled data. - Implement SAR techniques for displacement: amplitude change, coherence, and pixel-offset tracking to measure pipe and dune movement. - Develop dune-migration tracking (optical flow / feature tracking), migration direction, and mobility indices. - Engineer robust ingestion from Copernicus (CDSE / Sentinel Hub / STAC) and fuse optical, SAR, DEM, and ERA5 wind data. - Design labelling strategy (encroachment masks, severity) and a train/validation split that avoids leakage. - Communicate results and limitations honestly to technical and business stakeholders.

 

 

\## Required -7+ years applied data science / ML, with hands-on \*\*geospatial remote sensing\*\*. - Strong Python: numpy, rasterio/GDAL, xarray, scikit-image, geopandas/shapely. - Working knowledge of \*\*optical and SAR\*\* data (spectral indices, backscatter/dB, resolution trade-offs, revisit). - Deep learning with \*\*PyTorch\*\*; experience fine-tuning models (transfer learning, LoRA/PEFT). - Model validation and calibration: ROC/AUC, thresholding, cross-validation, handling weak/few labels.- Time-series / change-detection methods and coordinate reference systems (UTM, reprojection).

 

 

\## Nice to have - \*\*InSAR / SAR offset tracking\*\* (SNAP, ISCE, or equivalent) for surface/structure displacement. - Geospatial foundation models (Prithvi-EO, TerraTorch, HLS) and segmentation. - Copernicus/CDSE, Sentinel Hub, STAC, Planetary Computer. - Aeolian geomorphology / dune dynamics; oil & gas or pipeline-integrity domain exposure. - MLOps and cloud (containerisation, scheduled inference, geospatial data pipelines).

 

 

\## Qualifications - MSc/PhD in Remote Sensing, Geospatial Science, Earth Observation, CS/ML, Physics, or equivalent experience."""